OCAPIS: R Package for Ordinal Classification And Preprocessing In Scala. Heredia-Gómez, M. C., García, S., Gutiérrez, P. A., & Herrera, F.
OCAPIS: R Package for Ordinal Classification And Preprocessing In Scala [link]Paper  abstract   bibtex   
Ordinal Data are those where a natural order exist between the labels. The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nominal problems. However, that implies not taking into account their natural order constraints. In this paper, an innovative R package named ocapis (Ordinal Classification and Preprocessing In Scala) is introduced. Implemented mainly in Scala and available through Github, this library includes four learners and two pre-processing algorithms for ordinal and monotonic data. Main features of the package and examples of installation and use are explained throughout this manuscript.
@article{heredia-gomezOCAPISPackageOrdinal2018,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1810.09733},
  primaryClass = {cs, stat},
  title = {{{OCAPIS}}: {{R}} Package for {{Ordinal Classification And Preprocessing In Scala}}},
  url = {http://arxiv.org/abs/1810.09733},
  shorttitle = {{{OCAPIS}}},
  abstract = {Ordinal Data are those where a natural order exist between the labels. The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nominal problems. However, that implies not taking into account their natural order constraints. In this paper, an innovative R package named ocapis (Ordinal Classification and Preprocessing In Scala) is introduced. Implemented mainly in Scala and available through Github, this library includes four learners and two pre-processing algorithms for ordinal and monotonic data. Main features of the package and examples of installation and use are explained throughout this manuscript.},
  urldate = {2019-03-19},
  date = {2018-10-23},
  keywords = {Statistics - Machine Learning,Computer Science - Machine Learning},
  author = {Heredia-Gómez, M. Cristina and García, Salvador and Gutiérrez, Pedro Antonio and Herrera, Francisco},
  file = {/home/dimitri/Nextcloud/Zotero/storage/ERLUKYDP/Heredia-Gómez et al. - 2018 - OCAPIS R package for Ordinal Classification And P.pdf;/home/dimitri/Nextcloud/Zotero/storage/2VYVZLZ2/1810.html}
}
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